E‐Waste Recycling in a Developing Economy: How Knowledge and Anticipated Emotions Shape Consumer Intentions and <scp>WOM</scp>
Bibliographic record
Abstract
ABSTRACT Despite growing concern over electronic waste (e‐waste), critical gaps remain in understanding the psychological and informational drivers of e‐waste recycling behavior. This study draws on Risk Perception Theory and Appraisal Theory of Emotion to examine the relationships among e‐waste knowledge, perceived environmental risk, anticipated guilt and pride, recycling intention, and e‐waste‐related word of mouth (EW‐WOM). Survey data from 357 consumers in a developing economy were analyzed using PLS‐SEM. The results reveal that e‐waste knowledge significantly increases perceived environmental risk. Both emotions positively influence recycling intention and EW‐WOM. The study also introduces a validated e‐waste knowledge scale for future research. Practical implications include promoting e‐waste education across age groups and professions, and designing emotion‐driven public campaigns using storytelling and social media to increase recycling engagement. Policymakers and businesses can leverage these findings to craft targeted interventions that enhance knowledge and activate emotional motivators. By integrating informational and emotional strategies, this study offers a comprehensive framework for advancing sustainable consumer behavior in e‐waste management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".